This paper presents a thorough analysis of automatic classification applied to musical audio signals. The classification is based on a chosen set of machine learning algorithms. A database of 60 music composers/performers was prepared for the purpose of the described research. For each of the musicians, 15-20 music pieces were collected. All the pieces were partitioned into 20 segments and then parameterized. The feature vector consisted of 171 parameters, including MPEG-7 low-level descriptors and mel-frequency cepstral coefficients (MFCC) complemented with time-related dedicated parameters. The task of the classifier was to recognize the composer/performer and to properly categorize a selected piece of music. The paper also presents and discusses the results of classification.

Authors: Zwan, Pawel; Kostek, Bozena; Kupryjanow, Adam
Affiliation: Gdansk University of Technology, Gdansk, Poland
AES Convention: 130 (May 2011) Paper Number: 8449
Publication Date: May 13, 2011
Subject: Posters: Processing and Analysis

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